Visual function correlates of self-reported vision-related nighttime driving difficulties / La función visual se correlaciona con las dificultades de conducción nocturna relacionadas con la visión autoinformadas

Purpose: To investigate the visual function correlates of self-reported vision-related night driving difficulties among drivers. Methods: One hundred and seven drivers (age: 46.06 ± 8.24, visual acuity [VA] of 0.2logMAR or better) were included in the study. A standard vision and night driving questionnaire (VND-Q) was administered. VA and contrast sensitivity were measured under photopic and mesopic conditions. Mesopic VA was remeasured after introducing a peripheral glare source into the participants' field of view to enable computation of disability glare index. Regression analyses were used to assess the associations between VND-Q scores, and visual function measures. Results: The mean VND-Q score was -3.96±1.95 logit (interval scale score: 2.46±1.28). Simple linear regression models for photopic contrast sensitivity, mesopic VA, mesopic contrast sensitivity, and disability index significantly predicted VND-Q score (P<0.05), with mesopic VA and disability glare index accounting for the greatest variation (21 %) in VND-Q scores followed by photopic contrast sensitivity (19 %), and mesopic contrast sensitivity (15 %). A multiple regression model to determine the association between the predictors (photopic contrast sensitivity, mesopic VA, mesopic contrast sensitivity, and disability index) and VND-Q score yielded significant results, F (4, 102) = 8.58, P < 0.001, adj. R2 = 0.2224. Seeing dark-colored cars was the most challenging vision task. Conclusion: Changes in mesopic visual acuity, photopic and mesopic contrast sensitivity, as well as disability glare index are associated with and explain night driving-related visual difficulties. It is recommended to incorporate measurement of these visual functions into assessments related to driving performance.(AU)

Examining the impact of interventions in reducing self-reported engagement in distracted driving: A systematic review.

Despite the implementation of legal countermeasures, distracted driving remains a prevalent concern for road safety. This systematic review (following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines) summarised the literature on the impact of interventions targeting attitudes/intentions towards, and self-reported engagement in, distracted driving. Studies were eligible for this review if they examined self-reported behaviour/attitudes/intentions pertaining to distracted driving at baseline and post-intervention. Databases searched included PubMed, ProQuest, Scopus, and TRID. The review identified 19 articles/interventions, which were categorised into three intervention types. First, all program-based interventions (n = 6) reduced engagement in distracted driving. However, there were notable limitations to these studies, including a lack of control groups and difficulties implementing this intervention in a real-world setting. Second, active interventions (n = 9) were commonly utilised, yet a number of studies did not find any improvements in outcomes. Finally, four studies used a message-based intervention, with three studies reporting reduced intention and/or engagement in distracted driving. There is opportunity for message-based interventions to be communicated effortlessly online and target high-risk driving populations. However, further research is necessary to address limitations highlighted in the review, including follow-up testing and control groups. Implications are discussed with particular emphasis on areas where further research is needed.

Overtaking maneuvers on two-lane highways under the microscope: Exploration of a multidimensional framework for the analysis of safety, comfort and efficiency using simulator data.

An unreasonable overtaking attempt on two-lane highways could cause drivers to suffer in terms of driving safety, comfort, and efficiency. Several external factors related to the traffic environment (e.g., speed and car type of surrounding vehicles), were found to be the significant factors in drivers' overtaking performance in the previous studies. However, the microscopic decision-making (e.g., the moments of the occupation of the opposite lane) mechanisms during overtaking, by means of which drivers react to changes in the external traffic environment and adjust their overtaking trajectories, are still need to be explored. Hence, this study had three goals: (i) To explore the spatial characteristics of micro-decisions (MDs) (such as the start and end point) in overtaking trajectories; (ii) To measure three types of performance indicators (i.e., safety, comfort, and efficiency) for the execution of overtaking maneuvers; (iii) To quantitatively explain the microscopic decision-making mechanism in overtaking. Data for overtaking trajectories were collected from driving a simulation experiment where 52 Chinese student drivers completed a series of overtaking maneuvers on a typical two-lane highway under different traffic conditions. Two analyses were conducted: firstly, the distributions of the relative distance between the ego and surrounding vehicles at four key points (i.e., the start, entry, back, and end) in the overtaking trajectory were investigated and clustered to uncover the spatial characteristics of the MDs. Secondly, the safety, comfort, and efficiency of the overtaking were measured by the aggregations of multi-targets collision risks, triaxial acceleration variances, and spatial consumptions respectively based on the Data Envelopment Analysis (DEA), which were further applied in a two-stage SEM model to reveal the quantitative interrelationships among the external factors, microscope decisions and performances in overtaking. We confirmed that the MDs could be considered as the mediating variables between the external factors and overtaking performances. In the presence of the more hazardous traffic environment (e.g., faster traffic flow and impeded by a truck), the safety, comfort and efficiency of overtaking would be deteriorated inevitably. But drivers would execute the overtaking under the longer passing sight distance, migrate their trajectories forward, and shorten the spatial duration to significantly improve the overtaking performances. Based on this mechanism, a overtaking trajectory optimization strategy for the advanced or automatic driving system, was confirmed and concluded that 1) the passing gap should be firstly planned according to the sight distance acceptance of different drivers, which directly determine the upper limit of the safety performance in the overtaking; 2) the trajectory forward migration and shortening the whole duration in overtaking could be effective to enhance the overtaking performances of the overtaking on the two-lane highway; 3) the guidance of the stable control of the steering wheel and gas/brake pedals is essential in the overtaking.

Demographic and injury trends for car crash casualties hospitalized in Level I Trauma centers over two decades: data from the National Trauma Registry.

BACKGROUND: During the past two decades, there have been many changes in automotive and medical technologies, road infrastructure, trauma systems, and demographic changes which may have influenced injury outcomes. The aim of this study was to examine injury trends among traffic casualties, specifically private car occupants, hospitalized in Level I Trauma Centers (TC). METHODS: A retrospective cohort study was performed based on data from the Israel National Trauma Registry. The data included occupants of private cars hospitalized in all six Level I TC due to a traffic collision related injury between January 1, 1998 and December 31, 2019. Demographic, injury and hospitalization characteristics and in-hospital mortality were analyzed. Chi-squared (X2) test, multivariable logistic regression models and Spearman's rank correlation were used to analyze injury data and trends. RESULTS: During the study period, 21,173 private car occupants (14,078 drivers, 4,527 front passengers, and 2,568 rear passengers) were hospitalized due to a traffic crash. The percentage of females hospitalized due to a car crash increased from 37.7% in 1998 to 53.7% in 2019. Over a twofold increase in hospitalizations among older adult drivers (ages 65+) was observed, from 6.5% in 1998 to 15.7% in 2018 and 12.6% in 2019. While no increase was observed for severe traumatic brain injury, a statistically significant increase in severe abdominal and thoracic injuries was observed among the non-Jewish population along with a constant decrease in in-hospital mortality. CONCLUSIONS: This study provides interesting findings regarding injury and demographic trends among car occupants during the past two decades. Mortality among private car occupant casualties decreased during the study period, however an increase in serious abdominal and thoracic injuries was identified. The results should be used to design and implement policies and interventions for reducing injury and disability among car occupants.

Hospitalization information and burden of pediatric inpatients in transport accidents.

BACKGROUND: Transport accidents are one of the leading causes of child morbidity and mortality worldwide and represent a significant public health burden. This study aimed to investigate the hospitalization information and burden of pediatric inpatients in transport accidents in China. METHODS: In this study, we collected the cover page of the medical records of pediatric inpatients in transport accidents using the Futang Research Center of Pediatric Development (FRCPD) database from January 1, 2016 to December 31, 2021. Then, we extracted the epidemiological characteristics, including demographic characteristics, cases distribution, disease information, and hospitalization burden. RESULTS: Among 36,455 included inpatients, males, aged 1-3 years, East China, July were dominant in different subgroups. In transport accidents, pedestrians were the most frequently type of injury (65.69%). Of all known lesions, craniocerebral/nerve injury was the more common results in pediatric inpatients in transport accidents (33.93%). In addition to pedal cyclists more susceptible to sport system injury, other types of injured person with transport accidents were mainly craniocerebral/nerve injury. In terms of the type of discharge, occupant of heavy transport vehicle or bus and people with craniocerebral/nerve injury had the highest mortality rate after hospitalization in all type and lesion of injured person groups, respectively. The largest hospitalization burden in the type of injured person was occupant of heavy transport vehicle or bus. CONCLUSIONS: This study revealed that epidemiological characteristics and the main factor influencing the hospitalization information and burden of children with traffic accidents in China.

Quantifying alcohol's harm to others: a research and policy proposal.

Just under 2.5 million people die annually due to alcohol use. This global estimate, however, excludes most of the health burden borne by others than the alcohol user. Alcohol's harm to others includes a multitude of conditions, such as trauma from traffic crashes, fetal disorders due to prenatal exposure to alcohol, as well as interpersonal and intimate partner violence. While alcohol's causal role in these conditions is well-established, alcohol's harm to others' contribution to the overall health burden of alcohol remains unknown. This knowledge gap leads to a situation in which alcohol policy and prevention strategies largely focus on the reduction of alcohol's detrimental health harms on the alcohol users, neglecting affected others and population groups most vulnerable to these harms, including women and children. In this article, we seek to elucidate why estimates for alcohol's harm to others are lacking and offer guidance for future research. We also argue that a full assessment of the alcohol health burden that includes the harm caused by others' alcohol use would enhance the visibility and public awareness of such harms, and advancing the evaluation of policy interventions to mitigate them.

Chaque année, un peu moins de 2,5 millions de décès sont liés à la consommation d'alcool. Cette estimation globale ne tient cependant pas compte de l'impact sur la santé de l'entourage des consommateurs d'alcool. Les méfaits de l'alcool sur les autres ont une multitude de conséquences, parmi lesquelles des traumatismes dus aux accidents de la circulation, des anomalies fœtales liées à une exposition prénatale à l'alcool, ainsi que des actes de violence interpersonnelle et entre partenaires. Bien que le rôle causal de l'alcool dans ces problématiques soit bien établi, les répercussions de tels méfaits sur la santé dans son ensemble restent à déterminer. Des lacunes qui aboutissent souvent à une situation dans laquelle les politiques et stratégies de prévention se concentrent principalement sur la diminution des effets néfastes de l'alcool sur la santé des consommateurs eux-mêmes, négligeant les personnes qui les entourent et les catégories de population les plus vulnérables, en particulier les femmes et les enfants. Dans cet article, nous tentons d'expliquer pourquoi il n'existe aucune estimation concernant les méfaits de l'alcool sur les autres et prodiguons des conseils pour de futures recherches. Nous plaidons aussi pour une analyse complète de la charge sanitaire imputable à l'alcool incluant les méfaits de l'alcool sur les autres, afin d'améliorer la visibilité et de mieux sensibiliser l'opinion publique à ces problématiques, mais aussi de faire progresser l'évaluation des interventions politiques entreprises pour y remédier.

Cerca de 2,5 millones de personas mueren cada año por el consumo de alcohol. Sin embargo, esta estimación global excluye la mayor parte de la carga sanitaria que soportan personas que no son consumidores de alcohol. Los daños del alcohol a terceros incluyen multitud de afecciones, como los traumatismos por accidentes de tráfico, los trastornos fetales debidos a la exposición prenatal al alcohol, y la violencia interpersonal y de pareja. Aunque se sabe que el alcohol influye en estas afecciones, se desconoce la contribución de los daños del alcohol a terceros a la carga sanitaria global que supone el alcohol. Esta falta de conocimiento conduce a una situación en la que las estrategias de política y de prevención del alcohol se centran en gran medida en la reducción de los daños perjudiciales del alcohol para la salud de los consumidores de alcohol, dejando de lado a los demás afectados y a los grupos de población más vulnerables a estos daños, incluidas las mujeres y los niños. En este artículo, tratamos de dilucidar por qué faltan estimaciones sobre los daños del alcohol en otras personas y ofrecemos orientaciones para futuras investigaciones. También argumentamos que una evaluación completa de la carga sanitaria del alcohol que incluya los daños causados por el consumo de alcohol de otras personas mejoraría la visibilidad y la concienciación pública de esos daños, y haría avanzar la evaluación de las intervenciones políticas para mitigarlos.

Scheduling optimisation of alcohol test sites.

Drink driving is an infamous factor in road crashes and fatalities. Alcohol testing is a major countermeasure, and random breath tests (RBTs) deter tested drivers and passersby (observers who are not tested). We propose a genetic algorithm (GA)-based RBT scheduling optimisation method to achieve maximal deterrence of drink driving. The RBT schedule denotes the daily plan of where, when, and for how long tests should occur in the road network. The test results (positive and negative) and observing drivers are considered in the fitness function. The limited testing resource capacity is modeled by a number of constraints that consider the total duration of tests, the minimum and maximum duration of a single test site, and the total number of test sites during the day. Clustering of the alcohol-related crash data is used to estimate the matrix for drink driving on the scheduled day. The crash data and traffic flow data from Victoria, Australia are analysed and used to describe sober/drink driving. A detailed synthetic example is developed and a significant improvement with 150% more positive results and 59% more overall tests is observed using the proposed scheduling optimisation method.

Lightweight helmet target detection algorithm combined with Effici-Bi-Level Routing Attention.

Wearing helmets is essential in two-wheeler traffic to reduce the incidence of injuries caused by accidents. We present FB-YOLOv7, an improved detection network based on the YOLOv7-tiny model. The objective of this network is to tackle the problems of both missed detection and false detection that result from the difficulties in identifying small targets and the constraints in equipment performance during helmet detection. By applying an enhanced Bi-Level Routing Attention, the network can improve its capacity to extract global characteristics and reduce information distortion. Furthermore, we deploy the AFPN framework and effectively resolve information conflict using asymptotic adaptive feature fusion technology. Incorporating the EfficiCIoU loss significantly improves the prediction box's accuracy. Experimental trials done on specific datasets reveal that FB-YOLOv7 attains an accuracy of 87.2% and 94.6% on the mean average precision (mAP@.5). Additionally, it maintains a high level of efficiency with frame rates of 129 and 126 frames per second (FPS). FB-YOLOv7 surpasses the other six widely-used detection networks in terms of detection accuracy, network implementation requirements, sensitivity in detecting small targets, and potential for practical applications.

Incidence and predictors of mortality among road traffic accident victims admitted to hospitals at Hawassa city, Ethiopia.

BACKGROUND: Globally, road traffic accidents are the eighth-leading cause of death for all age groups. The estimated number of road traffic deaths in low income countries was more than three times as high as in high-income countries. Africa had the highest rate of fatalities attributed to road traffic accidents. Ethiopia has the highest number of road traffic fatalities among Sub-Saharan African countries. The main objective of this study was to determine the incidence and predictors of mortality among road traffic victims admitted to hospitals in Hawassa City. METHODS: A facility-based retrospective cohort study was conducted using secondary data from hospital records. A total of 398 road traffic accident victims admitted to selected hospitals in Hawassa city from January 2019 to December 2021 participated in the study. Data were analyzed using STATA version 14.1. The Cox regression model was used to determine the predictors of mortality. A hazard ratio with a 95% confidence interval and a cut-off value of P<0.05 was used to declare the risk and statistical significance, respectively. RESULT: The incidence rate of mortality for road traffic accident victims was 7.34 per 10,000 person-hours. The predictors of mortality were the value of GCS at admission <8 (aHR = 5.86; 95% CI: 2.00-17.19), GCS at admission 9-12 (aHR = 3.27; 95% CI: 1.28-8.40), the value of SBP at admission ≤89mmHg (aHR = 4.41; 95% CI: 2.22-8.77), admission to the ICU (aHR = 3.89; 95% CI: 1.83-8.28) and complications (aHR = 5.48; 95% CI: 2.74-10.01). CONCLUSION: The incidence of mortality among road traffic victims admitted to hospitals in Hawassa city was high. Thus, thorough follow-up and intensive management should be given to victims with critical health conditions.

Comparing fatal crash risk factors by age and crash type by using machine learning techniques.

This study aims to use machine learning methods to examine the causative factors of significant crashes, focusing on accident type and driver's age. In this study, a wide-ranging data set from Jeddah city is employed to look into various factors, such as whether the driver was male or female, where the vehicle was situated, the prevailing weather conditions, and the efficiency of four machine learning algorithms, specifically XGBoost, Catboost, LightGBM and RandomForest. The results show that the XGBoost Model (accuracy of 95.4%), the CatBoost model (94% accuracy), and the LightGBM model (94.9% accuracy) were superior to the random forest model with 89.1% accuracy. It is worth noting that the LightGBM had the highest accuracy of all models. This shows various subtle changes in models, illustrating the need for more analyses while assessing vehicle accidents. Machine learning is also a transforming tool in traffic safety analysis while providing vital guidelines for developing accurate traffic safety regulations.

Emergency Department Visits for Pedestrians Injured in Motor Vehicle Traffic Crashes - United States, January 2021-December 2023.

Traffic-related pedestrian deaths in the United States reached a 40-year high in 2021. Each year, pedestrians also suffer nonfatal traffic-related injuries requiring medical treatment. Near real-time emergency department visit data from CDC's National Syndromic Surveillance Program during January 2021-December 2023 indicated that among approximately 301 million visits identified, 137,325 involved a pedestrian injury (overall visit proportion = 45.62 per 100,000 visits). The proportions of visits for pedestrian injury were 1.53-2.47 times as high among six racial and ethnic minority groups as that among non-Hispanic White persons. Compared with persons aged ≥65 years, proportions among those aged 15-24 and 25-34 years were 2.83 and 2.61 times as high, respectively. The visit proportion was 1.93 times as high among males as among females, and 1.21 times as high during September-November as during June-August. Timely pedestrian injury data can help collaborating federal, state, and local partners rapidly monitor trends, identify disparities, and implement strategies supporting the Safe System approach, a framework for preventing traffic injuries among all road users.

The effects of ambient temperature on road traffic injuries in Jinan city: a time-stratified case-crossover study based on distributed lag nonlinear model.

Objectives: The impact of climate change, especially extreme temperatures, on health outcomes has become a global public health concern. Most previous studies focused on the impact of disease incidence or mortality, whereas much less has been done on road traffic injuries (RTIs). This study aimed to explore the effects of ambient temperature, particularly extreme temperature, on road traffic deaths in Jinan city. Methods: Daily data on road traffic deaths and meteorological factors were collected among all residents in Jinan city during 2011-2020. We used a time-stratified case-crossover design with distributed lag nonlinear model to evaluate the association between daily mean temperature, especially extreme temperature and road traffic deaths, and its variation in different subgroups of transportation mode, adjusting for meteorological confounders. Results: A total of 9,794 road traffic deaths were collected in our study. The results showed that extreme temperatures were associated with increased risks of deaths from road traffic injuries and four main subtypes of transportation mode, including walking, Bicycle, Motorcycle and Motor vehicle (except motorcycles), with obviously lag effects. Meanwhile, the negative effects of extreme high temperatures were significantly higher than those of extreme low temperatures. Under low-temperature exposure, the highest cumulative lag effect of 1.355 (95% CI, 1.054, 1.742) for pedal cyclists when cumulated over lag 0 to 6 day, and those for pedestrians, motorcycles and motor vehicle occupants all persisted until 14 days, with ORs of 1.227 (95% CI, 1.102, 1.367), 1.453 (95% CI, 1.214, 1.740) and 1.202 (95% CI, 1.005, 1.438), respectively. Under high-temperature exposure, the highest cumulative lag effect of 3.106 (95% CI, 1.646, 5.861) for motorcycle occupants when cumulated over lag 0 to 12 day, and those for pedestrian, pedal cyclists, and motor vehicle accidents all peaked when persisted until 14 days, with OR values of 1.638 (95% CI, 1.281, 2.094), 2.603 (95% CI, 1.695, 3.997) and 1.603 (95% CI, 1.066, 2.411), respectively. Conclusion: This study provides evidence that ambient temperature is significantly associated with the risk of road traffic injuries accompanied by obvious lag effect, and the associations differ by the mode of transportation. Our findings help to promote a more comprehensive understanding of the relationship between temperature and road traffic injuries, which can be used to establish appropriate public health policies and targeted interventions.

Major Traffic Safety Reform and Road Traffic Injuries Among Low-Income New York Residents, 2009-2021.

Objectives. To evaluate the effects of a comprehensive traffic safety policy-New York City's (NYC's) 2014 Vision Zero-on the health of Medicaid enrollees. Methods. We conducted difference-in-differences analyses using individual-level New York Medicaid data to measure traffic injuries and expenditures from 2009 to 2021, comparing NYC to surrounding counties without traffic reforms (n = 65 585 568 person-years). Results. After Vision Zero, injury rates among NYC Medicaid enrollees diverged from those of surrounding counties, with a net impact of 77.5 fewer injuries per 100 000 person-years annually (95% confidence interval = -97.4, -57.6). We observed marked reductions in severe injuries (brain injury, hospitalizations) and savings of $90.8 million in Medicaid expenditures over the first 5 years. Effects were largest among Black residents. Impacts were reversed during the COVID-19 period. Conclusions. Vision Zero resulted in substantial protection for socioeconomically disadvantaged populations known to face heightened risk of injury, but the policy's effectiveness decreased during the pandemic period. Public Health Implications. Many cities have recently launched Vision Zero policies and others plan to do so. This research adds to the evidence on how and in what circumstances comprehensive traffic policies protect public health. (Am J Public Health. 2024;114(6):633-641. https://doi.org/10.2105/AJPH.2024.307617).

Analyzing the transition from two-vehicle collisions to chain reaction crashes: A hybrid approach using random parameters logit model, interpretable machine learning, and clustering.

Chain reaction crashes (CRC) begin with a two-vehicle collision and rapidly intensify as more vehicles get directly involved. CRCs result in more extensive damage compared to two-vehicle crashes and understanding the progression of a two-vehicle collision into a CRC can unveil preventive strategies that have received less attention. In this study, to align with recent research direction and overcome the limitations of econometric and machine learning (ML) modelling, a hybrid approach is adopted. Moreover, to tackle the existing challenges in crash analysis, addressing unobserved heterogeneity in ML, and exploring random parameter effects and interactions more precisely, a new approach is proposed. To achieve this, a hybrid random parameter logit model and interpretable ML, joint with prior latent class clustering is implemented. Notably, this is the first attempt at using a clustering with hybrid modeling. The significant risk factors, their critical values, distinct effects, and interactions are interpreted using both marginal effects and the SHAP (SHapley Additive exPlanations) method across clusters. This study utilizes crash, traffic, and geometric data from eleven suburban freeways in Iran collected over a 5-year period. The overall results indicate an increased risk of CRC in congested traffic, higher traffic variation, and on horizontal curves combined with longitudinal slopes. Some parameters exhibit distinct or fluctuating effects, which are discussed across different conditions or considering interactions. For instance, during nighttime, heightened congestion on 2-lane freeways, increased traffic variation in less congested conditions, and adverse weather combined with horizontal curves and slopes pose risks. During daytime, increased traffic variation within highly congested sections, higher proportion of heavy vehicle traffic in moderately congested sections, and two lanes in each direction coupled with curves, elevate the levels of risk. The results of this study provide a better understanding of risk factors impact across different conditions, which are usable for policy makers.

The efficacy of hazard perception training and education: A systematic review and meta-analysis.

BACKGROUND: Hazard perception (HP) has been argued to improve with experience, with numerous training programs having been developed in an attempt to fast track the development of this critical safety skill. To date, there has been little synthesis of these methods. OBJECTIVE: The present study sought to synthesise the literature for all road users to capture the breadth of methodologies and intervention types, and quantify their efficacy. DATA SOURCES: A systematic review of both peer reviewed and non-peer-reviewed literature was completed. A total of 57 papers were found to have met inclusion criteria. RESULTS: Research into hazard perception has focused primarily on drivers (with 42 studies), with a limited number of studies focusing on vulnerable road users, including motorcyclists (3 studies), cyclists (7 studies) and pedestrians (5 studies). Training was found to have a large significant effect on improving hazard perception skills for drivers (g = 0.78) and cyclists (g = 0.97), a moderate effect for pedestrians (g = 0.64) and small effect for motorcyclists (g = 0.42). There was considerable heterogeneity in the findings, with the efficacy of training varying as a function of the hazard perception skill being measured, the type of training enacted (active, passive or combined) and the number of sessions of training (single or multiple). Active training and single sessions were found to yield more consistent significant improvements in hazard perception. CONCLUSIONS: This study found that HP training improved HP skill across all road user groups with generally moderate to large effects identified. HP training should employ a training method that actively engages the participants in the training task. Preliminary results suggest that a single session of training may be sufficient to improve HP skill however more research is needed into the delivery of these single sessions and long-term retention. Further research is also required to determine whether improvements in early-stage skills translate to improvements in responses on the road, and the long-term retention of the skills developed through training.

Modeling distracted driving behavior considering cognitive processes.

The modeling of distracted driving behavior has been studied for many years, however, there remain many distraction phenomena that can not be fully modeled. This study proposes a new method that establishes the model using the queuing network model human processor (QN-MHP) framework. Unlike previous models that only consider distracted-driving-related human factors from a mathematical perspective, the proposed method reflects the information processing in the human brain, and simulates the distracted driver's cognitive processes based on a model structure supported by physiological and cognitive research evidence. Firstly, a cumulative activation effect model for external stimuli is adopted to mimic the phenomenon that a driver responds only to stimuli above a certain threshold. Then, dual-task queuing and switching mechanisms are modeled to reflect the cognitive resource allocation under distraction. Finally, the driver's action is modeled by the Intelligent Driver Model (IDM). The model is developed for visual distraction auditory distraction separately. 773 distracted car-following events from the Shanghai Naturalistic Driving Study data were used to calibrate and verify the model. Results show that the model parameters are more uniform and reasonable. Meanwhile, the model accuracy has improved by 57% and 66% compared to the two baseline models respectively. Moreover, the model demonstrates its ability to generate critical pre-crash scenarios and estimate the crash rate of distracted driving. The proposed model is expected to contribute to safety research regarding new vehicle technologies and traffic safety analysis.

The development of a road safety policy index and its application in evaluating the effects of road safety policy.

The paper presents an exploratory study of a road safety policy index developed for Norway. The index consists of ten road safety measures for which data on their use from 1980 to 2021 are available. The ten measures were combined into an index which had an initial value of 50 in 1980 and increased to a value of 185 in 2021. To assess the application of the index in evaluating the effects of road safety policy, negative binomial regression models and multivariate time series models were developed for traffic fatalities, fatalities and serious injuries, and all injuries. The coefficient for the policy index was negative, indicating the road safety policy has contributed to reducing the number of fatalities and injuries. The size of this contribution can be estimated by means of at least three estimators that do not always produce identical values. There is little doubt about the sign of the relationship: a stronger road safety policy (as indicated by index values) is associated with a larger decline in fatalities and injuries. A precise quantification is, however, not possible. Different estimators of effect, all of which can be regarded as plausible, yield different results.

[Prevalence and predictors of post-traumatic stress disorder in road traffic accidents]. / Prévalence et facteurs prédicteurs des troubles post-traumatiques chez les accidentés de la voie publique.

Introduction: trauma-related disorders following a road accident have both a health and an economic impact. Methods: we conducted a prospective study to determine the prevalence of these disorders, and to identify risk factors in subjects victims of road accidents and hospitalized in the Department of Orthopedic Surgery and Traumatology of the University Hospital Center of Sfax-Tunisia. Results: a total of sixty-ten subjects were included in this study. The prevalence of acute stress disorder was 37.1% and was associated with female sex, low educational level, previous medical and surgical history, passivity during the accident, severity of injuries and the presence of anxious and depressive symptoms. Post-traumatic stress disorder was observed in 40% of subjects and was associated with urban residential environment, passivity during the accident and anxious and depressive symptoms. Low scores for functional coping strategies and high scores for dysfunctional coping strategies were significantly associated with both disorders. Low educational level, urban residential environment, high levels of anxiety and depression, and denial coping strategy appear to be independent risk factors for acute stress and post-traumatic stress disorder. Conclusion: It is therefore important to determine the profile of people at greater risk of post-traumatic stress disorder, to enable early diagnosis in victims of road accidents.